Modeling time correlation in passive network loss tomography

We consider the problem of inferring link loss rates using passive measurements. Prior inference approaches are mainly built on the time correlation nature of packet losses. However, passive inference generally has limited control over the measurement process, and it is a challenging issue to adapt loss rate inference to the impact of time correlation. We address this issue and propose a new loss model that expresses an inferred link loss rate as a function of time correlation. Under this loss model with time correlation, we show its identifiability, and propose a novel profile-likelihood-based inference approach that can accurately infer link loss rates for various complex topologies (e.g., trees with many leaf branches). We validate the accuracy of our inference approach with model and network simulations.